From 4d6da56212f7638e7ccd1aa9cbfc1ee697115542 Mon Sep 17 00:00:00 2001
From: Zheyuan Wu <60459821+Trance-0@users.noreply.github.com>
Date: Thu, 10 Apr 2025 13:25:42 -0500
Subject: [PATCH] Create CSE559A_L22.md

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+# CSE559A Lecture 22
+
+## Continue on Robust Fitting of parametric models
+
+### RANSAC
+
+#### Definition: RANdom SAmple Consensus
+
+RANSAC is a method to fit a model to a set of data points.
+
+It is a non-deterministic algorithm that can be used to fit a model to a set of data points.
+
+Pros:
+
+- Simple and general 
+- Applicable to many different problems
+- Often works well in practice
+
+Cons:
+
+- Lots of parameters to set
+- Number of iterations grows exponentially as outlier ratio increases
+- Can't always get a good initialization of the model based on the minimum number of samples.
+
+### Hough Transform
+
+Use point-line duality to find lines.
+
+In practice, we don't use (m,b) parameterization.
+
+Instead, we use polar parameterization:
+
+
+Algorithm outline:
+
+- Initialize accumulator $H$ to all zeros
+- For each feature point $(x_i, y_i)$
+    - For $\theta=0$ to $180$
+    - $\rho=x_i \cos \theta + y_i \sin \theta$
+
+Noise makes the peak fuzzy.
+